Impact of SLC20A1 on the Wnt/Β‑Catenin Signaling Pathway in Somatotroph Adenomas
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Supplemental Figure 1. Vimentin
Double mutant specific genes Transcript gene_assignment Gene Symbol RefSeq FDR Fold- FDR Fold- FDR Fold- ID (single vs. Change (double Change (double Change wt) (single vs. wt) (double vs. single) (double vs. wt) vs. wt) vs. single) 10485013 BC085239 // 1110051M20Rik // RIKEN cDNA 1110051M20 gene // 2 E1 // 228356 /// NM 1110051M20Ri BC085239 0.164013 -1.38517 0.0345128 -2.24228 0.154535 -1.61877 k 10358717 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 /// BC 1700025G04Rik NM_197990 0.142593 -1.37878 0.0212926 -3.13385 0.093068 -2.27291 10358713 NM_197990 // 1700025G04Rik // RIKEN cDNA 1700025G04 gene // 1 G2 // 69399 1700025G04Rik NM_197990 0.0655213 -1.71563 0.0222468 -2.32498 0.166843 -1.35517 10481312 NM_027283 // 1700026L06Rik // RIKEN cDNA 1700026L06 gene // 2 A3 // 69987 /// EN 1700026L06Rik NM_027283 0.0503754 -1.46385 0.0140999 -2.19537 0.0825609 -1.49972 10351465 BC150846 // 1700084C01Rik // RIKEN cDNA 1700084C01 gene // 1 H3 // 78465 /// NM_ 1700084C01Rik BC150846 0.107391 -1.5916 0.0385418 -2.05801 0.295457 -1.29305 10569654 AK007416 // 1810010D01Rik // RIKEN cDNA 1810010D01 gene // 7 F5 // 381935 /// XR 1810010D01Rik AK007416 0.145576 1.69432 0.0476957 2.51662 0.288571 1.48533 10508883 NM_001083916 // 1810019J16Rik // RIKEN cDNA 1810019J16 gene // 4 D2.3 // 69073 / 1810019J16Rik NM_001083916 0.0533206 1.57139 0.0145433 2.56417 0.0836674 1.63179 10585282 ENSMUST00000050829 // 2010007H06Rik // RIKEN cDNA 2010007H06 gene // --- // 6984 2010007H06Rik ENSMUST00000050829 0.129914 -1.71998 0.0434862 -2.51672 -
Phosphate Transporters of the SLC20 and SLC34 Families
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2013 Phosphate transporters of the SLC20 and SLC34 families Forster, Ian C ; Hernando, Nati ; Biber, Jürg ; Murer, Heini Abstract: Transport of inorganic phosphate (Pi) across the plasma membrane is essential for normal cellular function. Members of two families of SLC proteins (SLC20 and SLC34) act as Na(+)-dependent, secondary-active cotransporters to transport Pi across cell membranes. The SLC34 proteins are ex- pressed in specific organs important for Pi homeostasis: NaPi-IIa (SLC34A1) and NaPi-IIc (SLC34A3) fulfill essential roles in Pi reabsorption in the kidney proximal tubule and NaPi-IIb (SLC34A2) medi- ates Pi absorption in the gut. The SLC20 proteins, PiT-1 (SLC20A1), PiT-2 (SLC20A2) are expressed ubiquitously in all tissues and although generally considered as ”housekeeping” transport proteins, the discovery of tissue-specific activity, regulatory pathways and gene-related pathophysiologies, is redefining their importance. This review summarizes our current knowledge of SLC20 and SLC34 proteins in terms of their basic molecular characteristics, physiological roles, known pathophysiology and pharmacology. DOI: https://doi.org/10.1016/j.mam.2012.07.007 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-79439 Journal Article Accepted Version Originally published at: Forster, Ian C; Hernando, Nati; Biber, Jürg; Murer, Heini (2013). Phosphate transporters of the SLC20 and SLC34 families. Molecular Aspects of Medicine, 34(2-3):386-395. DOI: https://doi.org/10.1016/j.mam.2012.07.007 1 Mini-reviews Series-Molecular Aspects of Medicine (JMAM) Phosphate transporters of the SLC20 and SLC34 families Ian C. -
Upregulation of Peroxisome Proliferator-Activated Receptor-Α And
Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer Chih-Yang Wang, Ying-Jui Chao, Yi-Ling Chen, Tzu-Wen Wang, Nam Nhut Phan, Hui-Ping Hsu, Yan-Shen Shan, Ming-Derg Lai 1 Supplementary Table 1. Demographics and clinical outcomes of five patients with ampullary cancer Time of Tumor Time to Age Differentia survival/ Sex Staging size Morphology Recurrence recurrence Condition (years) tion expired (cm) (months) (months) T2N0, 51 F 211 Polypoid Unknown No -- Survived 193 stage Ib T2N0, 2.41.5 58 F Mixed Good Yes 14 Expired 17 stage Ib 0.6 T3N0, 4.53.5 68 M Polypoid Good No -- Survived 162 stage IIA 1.2 T3N0, 66 M 110.8 Ulcerative Good Yes 64 Expired 227 stage IIA T3N0, 60 M 21.81 Mixed Moderate Yes 5.6 Expired 16.7 stage IIA 2 Supplementary Table 2. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of an ampullary cancer microarray using the Database for Annotation, Visualization and Integrated Discovery (DAVID). This table contains only pathways with p values that ranged 0.0001~0.05. KEGG Pathway p value Genes Pentose and 1.50E-04 UGT1A6, CRYL1, UGT1A8, AKR1B1, UGT2B11, UGT2A3, glucuronate UGT2B10, UGT2B7, XYLB interconversions Drug metabolism 1.63E-04 CYP3A4, XDH, UGT1A6, CYP3A5, CES2, CYP3A7, UGT1A8, NAT2, UGT2B11, DPYD, UGT2A3, UGT2B10, UGT2B7 Maturity-onset 2.43E-04 HNF1A, HNF4A, SLC2A2, PKLR, NEUROD1, HNF4G, diabetes of the PDX1, NR5A2, NKX2-2 young Starch and sucrose 6.03E-04 GBA3, UGT1A6, G6PC, UGT1A8, ENPP3, MGAM, SI, metabolism -
Figure S1. DMD Module Network. the Network Is Formed by 260 Genes from Disgenet and 1101 Interactions from STRING. Red Nodes Are the Five Seed Candidate Genes
Figure S1. DMD module network. The network is formed by 260 genes from DisGeNET and 1101 interactions from STRING. Red nodes are the five seed candidate genes. Figure S2. DMD module network is more connected than a random module of the same size. It is shown the distribution of the largest connected component of 10.000 random modules of the same size of the DMD module network. The green line (x=260) represents the DMD largest connected component, obtaining a z-score=8.9. Figure S3. Shared genes between BMD and DMD signature. A) A meta-analysis of three microarray datasets (GSE3307, GSE13608 and GSE109178) was performed for the identification of differentially expressed genes (DEGs) in BMD muscle biopsies as compared to normal muscle biopsies. Briefly, the GSE13608 dataset included 6 samples of skeletal muscle biopsy from healthy people and 5 samples from BMD patients. Biopsies were taken from either biceps brachii, triceps brachii or deltoid. The GSE3307 dataset included 17 samples of skeletal muscle biopsy from healthy people and 10 samples from BMD patients. The GSE109178 dataset included 14 samples of controls and 11 samples from BMD patients. For both GSE3307 and GSE10917 datasets, biopsies were taken at the time of diagnosis and from the vastus lateralis. For the meta-analysis of GSE13608, GSE3307 and GSE109178, a random effects model of effect size measure was used to integrate gene expression patterns from the two datasets. Genes with an adjusted p value (FDR) < 0.05 and an │effect size│>2 were identified as DEGs and selected for further analysis. A significant number of DEGs (p<0.001) were in common with the DMD signature genes (blue nodes), as determined by a hypergeometric test assessing the significance of the overlap between the BMD DEGs and the number of DMD signature genes B) MCODE analysis of the overlapping genes between BMD DEGs and DMD signature genes. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
The Concise Guide to Pharmacology 2019/20
Edinburgh Research Explorer THE CONCISE GUIDE TO PHARMACOLOGY 2019/20 Citation for published version: Cgtp Collaborators 2019, 'THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Transporters', British Journal of Pharmacology, vol. 176 Suppl 1, pp. S397-S493. https://doi.org/10.1111/bph.14753 Digital Object Identifier (DOI): 10.1111/bph.14753 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: British Journal of Pharmacology General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 28. Sep. 2021 S.P.H. Alexander et al. The Concise Guide to PHARMACOLOGY 2019/20: Transporters. British Journal of Pharmacology (2019) 176, S397–S493 THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Transporters Stephen PH Alexander1 , Eamonn Kelly2, Alistair Mathie3 ,JohnAPeters4 , Emma L Veale3 , Jane F Armstrong5 , Elena Faccenda5 ,SimonDHarding5 ,AdamJPawson5 , Joanna L -
Type of the Paper (Article
Supplementary Material A Proteomics Study on the Mechanism of Nutmeg-induced Hepatotoxicity Wei Xia 1, †, Zhipeng Cao 1, †, Xiaoyu Zhang 1 and Lina Gao 1,* 1 School of Forensic Medicine, China Medical University, Shenyang 110122, P. R. China; lessen- [email protected] (W.X.); [email protected] (Z.C.); [email protected] (X.Z.) † The authors contributed equally to this work. * Correspondence: [email protected] Figure S1. Table S1. Peptide fraction separation liquid chromatography elution gradient table. Time (min) Flow rate (mL/min) Mobile phase A (%) Mobile phase B (%) 0 1 97 3 10 1 95 5 30 1 80 20 48 1 60 40 50 1 50 50 53 1 30 70 54 1 0 100 1 Table 2. Liquid chromatography elution gradient table. Time (min) Flow rate (nL/min) Mobile phase A (%) Mobile phase B (%) 0 600 94 6 2 600 83 17 82 600 60 40 84 600 50 50 85 600 45 55 90 600 0 100 Table S3. The analysis parameter of Proteome Discoverer 2.2. Item Value Type of Quantification Reporter Quantification (TMT) Enzyme Trypsin Max.Missed Cleavage Sites 2 Precursor Mass Tolerance 10 ppm Fragment Mass Tolerance 0.02 Da Dynamic Modification Oxidation/+15.995 Da (M) and TMT /+229.163 Da (K,Y) N-Terminal Modification Acetyl/+42.011 Da (N-Terminal) and TMT /+229.163 Da (N-Terminal) Static Modification Carbamidomethyl/+57.021 Da (C) 2 Table S4. The DEPs between the low-dose group and the control group. Protein Gene Fold Change P value Trend mRNA H2-K1 0.380 0.010 down Glutamine synthetase 0.426 0.022 down Annexin Anxa6 0.447 0.032 down mRNA H2-D1 0.467 0.002 down Ribokinase Rbks 0.487 0.000 -
9-Azido Analogs of Three Sialic Acid Forms for Metabolic Remodeling Of
Supporting Information 9-Azido Analogs of Three Sialic Acid Forms for Metabolic Remodeling of Cell-Surface Sialoglycans Bo Cheng,†,‡ Lu Dong,†,§ Yuntao Zhu,†,‡ Rongbing Huang,†,‡ Yuting Sun,†,‖ Qiancheng You,†,‡ Qitao Song,†,§ James C. Paton, ∇ Adrienne W. Paton,∇ and Xing Chen*,†,‡,§,⊥,# †College of Chemistry and Molecular Engineering, ‡Beijing National Laboratory for Molecular Sciences, §Peking−Tsinghua Center for Life Sciences,‖Academy for Advanced Interdisciplinary Studies, ⊥Synthetic and Functional Biomolecules Center, and #Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing 100871, China ∇Research Centre for Infectious Diseases, Department of Molecular and Biomedical Science, University of Adelaide, Adelaide SA 5005, Australia Page S1 Table of Contents: Scheme S1.……………………………………………………….........……………. S3 Figure S1……………………………………………………..………..……………. S3 Figure S2……………………………………………………..………..…………… S4 Figure S3……………………………………………………..………..…………… S4 Figure S4……………………………………………………..………..…………… S5 Figure S5……………………………………………………..………..…………… S6 Figure S6……………………………………………………..………..…………….S7 Figure S7……………………………………………………..………..…………….S8 Figure S8……………………………………………………..………..…………….S9 Experimental Procedures……………………………….…........…………....S10-S27 Table S1………………………………………………..………..…………….S28-S48 Supporting Reference……………………………………………….......………...S49 Page S2 Scheme S1. Synthesis of 9AzNeu5Gc Figure S1: a, b, c, d) Representative scatter plots (FSC vs. SSC) and histograms of flow cytometry analysis -
Epistasis-Driven Identification of SLC25A51 As a Regulator of Human
ARTICLE https://doi.org/10.1038/s41467-020-19871-x OPEN Epistasis-driven identification of SLC25A51 as a regulator of human mitochondrial NAD import Enrico Girardi 1, Gennaro Agrimi 2, Ulrich Goldmann 1, Giuseppe Fiume1, Sabrina Lindinger1, Vitaly Sedlyarov1, Ismet Srndic1, Bettina Gürtl1, Benedikt Agerer 1, Felix Kartnig1, Pasquale Scarcia 2, Maria Antonietta Di Noia2, Eva Liñeiro1, Manuele Rebsamen1, Tabea Wiedmer 1, Andreas Bergthaler1, ✉ Luigi Palmieri2,3 & Giulio Superti-Furga 1,4 1234567890():,; About a thousand genes in the human genome encode for membrane transporters. Among these, several solute carrier proteins (SLCs), representing the largest group of transporters, are still orphan and lack functional characterization. We reasoned that assessing genetic interactions among SLCs may be an efficient way to obtain functional information allowing their deorphanization. Here we describe a network of strong genetic interactions indicating a contribution to mitochondrial respiration and redox metabolism for SLC25A51/MCART1, an uncharacterized member of the SLC25 family of transporters. Through a combination of metabolomics, genomics and genetics approaches, we demonstrate a role for SLC25A51 as enabler of mitochondrial import of NAD, showcasing the potential of genetic interaction- driven functional gene deorphanization. 1 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. 2 Laboratory of Biochemistry and Molecular Biology, Department of Biosciences, Biotechnologies and Biopharmaceutics, -
Human Induced Pluripotent Stem Cell–Derived Podocytes Mature Into Vascularized Glomeruli Upon Experimental Transplantation
BASIC RESEARCH www.jasn.org Human Induced Pluripotent Stem Cell–Derived Podocytes Mature into Vascularized Glomeruli upon Experimental Transplantation † Sazia Sharmin,* Atsuhiro Taguchi,* Yusuke Kaku,* Yasuhiro Yoshimura,* Tomoko Ohmori,* ‡ † ‡ Tetsushi Sakuma, Masashi Mukoyama, Takashi Yamamoto, Hidetake Kurihara,§ and | Ryuichi Nishinakamura* *Department of Kidney Development, Institute of Molecular Embryology and Genetics, and †Department of Nephrology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan; ‡Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, Hiroshima, Japan; §Division of Anatomy, Juntendo University School of Medicine, Tokyo, Japan; and |Japan Science and Technology Agency, CREST, Kumamoto, Japan ABSTRACT Glomerular podocytes express proteins, such as nephrin, that constitute the slit diaphragm, thereby contributing to the filtration process in the kidney. Glomerular development has been analyzed mainly in mice, whereas analysis of human kidney development has been minimal because of limited access to embryonic kidneys. We previously reported the induction of three-dimensional primordial glomeruli from human induced pluripotent stem (iPS) cells. Here, using transcription activator–like effector nuclease-mediated homologous recombination, we generated human iPS cell lines that express green fluorescent protein (GFP) in the NPHS1 locus, which encodes nephrin, and we show that GFP expression facilitated accurate visualization of nephrin-positive podocyte formation in -
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Supplemental Information Biological and Pharmaceutical Bulletin Promoter Methylation Profiles between Human Lung Adenocarcinoma Multidrug Resistant A549/Cisplatin (A549/DDP) Cells and Its Progenitor A549 Cells Ruiling Guo, Guoming Wu, Haidong Li, Pin Qian, Juan Han, Feng Pan, Wenbi Li, Jin Li, and Fuyun Ji © 2013 The Pharmaceutical Society of Japan Table S1. Gene categories involved in biological functions with hypomethylated promoter identified by MeDIP-ChIP analysis in lung adenocarcinoma MDR A549/DDP cells compared with its progenitor A549 cells Different biological Genes functions transcription factor MYOD1, CDX2, HMX1, THRB, ARNT2, ZNF639, HOXD13, RORA, FOXO3, HOXD10, CITED1, GATA1, activity HOXC6, ZGPAT, HOXC8, ATOH1, FLI1, GATA5, HOXC4, HOXC5, PHTF1, RARB, MYST2, RARG, SIX3, FOXN1, ZHX3, HMG20A, SIX4, NR0B1, SIX6, TRERF1, DDIT3, ASCL1, MSX1, HIF1A, BAZ1B, MLLT10, FOXG1, DPRX, SHOX, ST18, CCRN4L, TFE3, ZNF131, SOX5, TFEB, MYEF2, VENTX, MYBL2, SOX8, ARNT, VDR, DBX2, FOXQ1, MEIS3, HOXA6, LHX2, NKX2-1, TFDP3, LHX6, EWSR1, KLF5, SMAD7, MAFB, SMAD5, NEUROG1, NR4A1, NEUROG3, GSC2, EN2, ESX1, SMAD1, KLF15, ZSCAN1, VAV1, GAS7, USF2, MSL3, SHOX2, DLX2, ZNF215, HOXB2, LASS3, HOXB5, ETS2, LASS2, DLX5, TCF12, BACH2, ZNF18, TBX21, E2F8, PRRX1, ZNF154, CTCF, PAX3, PRRX2, CBFA2T2, FEV, FOS, BARX1, PCGF2, SOX15, NFIL3, RBPJL, FOSL1, ALX1, EGR3, SOX14, FOXJ1, ZNF92, OTX1, ESR1, ZNF142, FOSB, MIXL1, PURA, ZFP37, ZBTB25, ZNF135, HOXC13, KCNH8, ZNF483, IRX4, ZNF367, NFIX, NFYB, ZBTB16, TCF7L1, HIC1, TSC22D1, TSC22D2, REXO4, POU3F2, MYOG, NFATC2, ENO1, -
Genomic and Transcriptome Analysis Revealing an Oncogenic Functional Module in Meningiomas
Neurosurg Focus 35 (6):E3, 2013 ©AANS, 2013 Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas XIAO CHANG, PH.D.,1 LINGLING SHI, PH.D.,2 FAN GAO, PH.D.,1 JONATHAN RUssIN, M.D.,3 LIYUN ZENG, PH.D.,1 SHUHAN HE, B.S.,3 THOMAS C. CHEN, M.D.,3 STEVEN L. GIANNOTTA, M.D.,3 DANIEL J. WEISENBERGER, PH.D.,4 GAbrIEL ZADA, M.D.,3 KAI WANG, PH.D.,1,5,6 AND WIllIAM J. MAck, M.D.1,3 1Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California; 2GHM Institute of CNS Regeneration, Jinan University, Guangzhou, China; 3Department of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California; 4USC Epigenome Center, Keck School of Medicine, University of Southern California, Los Angeles, California; 5Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, California; and 6Division of Bioinformatics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California Object. Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%–5% display malignant pathological features. The key molecular pathways involved in malignant trans- formation remain to be determined. Methods. Illumina expression microarrays were used to assess gene expression levels, and Illumina single- nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant me- ningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones).